Tag Archives: building analytics culture

I’ve been meandering through an extended series on digital transformation: why it’s hard, where things go wrong, and what you need to be able to do to be successful. In this post, I intend to summarize some of that thinking and describe how the large enterprise should organize itself to be good at digital.

Throughout this series, I’ve emphasized the importance of being able to make good decisions in the digital realm. That is, of course, the function of analytics and its my own special concerns when it comes to digital. But there are people who will point out that decision-making is not the be all and end all of digital excellence. They might suggest that being able to execute is important too.

If you’re a football fan, it’s easy to see the dramatic difference between Peyton Manning – possibly the finest on-field decision-maker in the history of the game – with a good arm and without. It’s one thing to know where to throw the ball on any given play, quite another to be able to get it there accurately. If that wasn’t the case, it’s probably true that many of my readers would be making millions in the NFL!

On the other hand, this divide between decision-making and execution tends to break down if you extend your view to the entire organization. If the GM is doing the job properly, then the decision about which quarterbacks to draft or sign will appropriately balance their physical and decision-making skills. That’s part of what’s involved in good GM decisioning. Meanwhile, the coach has an identical responsibility on a day-to-day basis. A foot injury may limit Peyton to the point where his backup becomes a better option. Then it may heal and the pendulum swings back. The organization makes a series of decisions and if it can make all of those decisions well, then it’s hard to see how execution doesn’t follow along.

If, as an organization, I can make good decisions about the strategy for digital, the technology to run it on, the agencies to build it, the people to optimize it, the way to organize it, and the tactics to drive it, then everything is likely to be pretty good.

Unfortunately, it’s simply not the case that the analytics, organization and capabilities necessary to make good decisions across all these areas are remotely similar. To return to my football analogy, it’s clear that very few organizations are setup to make good decisions in every aspect of their operations. Some organizations excel at particular functions (like game-planning) but are very poor at drafting. Indeed, sometimes success in one-area breeds disaster in another. When a coach like Chip Kelly becomes very successful in his role, there is a tendency for the organization to expand that role so that the coach has increasing control over personnel. This almost always works badly in practice. Even knowing it will work badly doesn’t prevent the problem. Since the coach is so important, it may be that an organization will cede much control over personnel to a successful coach even when everyone (except the coach) believes it’s a bad idea.

If you don’t think similar situations arise constantly in corporate America, you aren’t paying attention.

In my posts in this series, I’ve mapped out the capabilities necessary to give decision-makers the information and capabilities they need to make good decisions about digital experiences. I haven’t touched on (and don’t really intend to touch on) broader themes like deciding who the right people to hire are or what kind of measurement, analysis or knowledge is necessary to make those sorts of meta-decisions.

There are two respects, however, in which I have tried to address at least some of these meta-concerns about execution. First, I’ve described why it is and how it comes to pass that most enterprises don’t use analytics to support strategic decision-making. This seems like a clear miss and a place where thoughtful implementation of good measurement, particularly voice-of-customer measurement of the type I’ve described, should yield high returns.

Second, I took a stab at describing how organizations can think about and work toward building an analytics culture. In these two posts, I argue that most attempts at culture-building approach the problem backwards. The most common culture-building activities in the enterprise are all about “talk”. We talk about diversity. We talk about ethics. We talk about being data-driven in our decision-making. I don’t think this talk adds up to much. I suggest that culture is formed far more through habit than talk; that if an organization wants to build an analytics culture, it needs to find ways to “do” analytics. The word may proceed the deed, but it is only through the force of the deed (good habits) that the word becomes character/culture. This may seem somewhat obvious – no, it is obvious – but people somehow manage to miss the obvious far too often. Those posts don’t just formulate the obvious, they also suggest a set of activities that are particularly efficacious in creating good enterprise habits of decision-making. If you care about enterprise culture and you haven’t already done so, give them a read.

For some folks, however, all these analytics actions miss the key questions. They don’t want to know what the organization should do. They want to know how the organization should work. Who owns digital? Who owns analytics? What lives in a central organization? What lives in a business unit? Is digital a capability or a department?

In the context of the small company, most of these questions aren’t terribly important. In the large enterprise, they mean a lot. But acknowledging that they mean a lot isn’t to suggest that I can answer them – or at least most of them.

I’m skeptical that there is an answer for most of these questions. At least in the abstract, I doubt there is one right organization for digital or one right degree of centralization. I’ve had many conversations with wise folks who recognize that their organizations seem to be in constant motion – swinging like an enormous pendulum between extremes of centralization followed by extremes of decentralization.

Even this peripatetic motion – which can look so irrational from the inside – may make sense. If we assume that centralization and decentralization have distinct advantages, then not only might it be true that changing circumstances might drive a change in the optimal configuration, but it might even be true that swinging the organization from one pole to the other might help capture the benefits of each.

That seems unlikely, but you never know. There is sometimes more logic in the seemingly irrational movements of the crowd than we might first imagine.

Most questions about digital organization are deeply historical. They depend on what type of company you are, in what of market, with what culture and what strategic imperatives. All of which is, of course, Management 101. Obvious stuff that hardly needs to be stated.

However, there are some aspects of digital about which I am willing to be more directive. First, that some balance between centralization and decentralization is essential in analytics. The imperative for centralization is driven by these factors: the need for comparative metrics of success around digital, the need for consistent data collection, the imperatives of the latest generation of highly-complex IT systems, and the need/desire to address customers across the full spectrum of their engagement with the enterprise. Of these, the first and the last are primary. If you don’t need those two, then you may not care about consistent data collection or centralized data systems (this last is debatable).

On the other hand, there are powerful reasons for decentralization of which the biggest is simply that analytics is best done as close to the decision-making as possible. Before the advent of Hadoop, I would have suggested that the vast majority of analytics resources in the digital space be decentralized. Hadoop makes that much harder. The skills are much rarer, the demands for control and governance much higher, and the need for cross-domain expertise much greater in this new world.

That will change. As the open-source analytics stack matures and the market over-rewards skilled practitioners – drawing in more folks, it will become much easier to decentralize again. This isn’t the first time we’ve been down the IT path that goes from centralization to gradual diffusion as technologies become cheaper, easier, and better supported.

At an even more fundamental level than the question of centralization lives the location and nature of digital. Is digital treated as a thing? Is it part of Marketing? Or Operations? Or does each thing have a digital component?

I know I should have more of an opinion about this, but I’m afraid that the right answers seem to me, once again, to be local and historical. In a digital pure-play, to even speak of digital as a thing seems absurd. It’s the core of the company. In a gas company, on the other hand, digital might best be viewed as a customer service channel. In a manufacturer, digital might be a sub-function of brand marketing or, depending on the nature of the digital investment and its importance to the company, a unit unto-itself.

Obviously, one of the huge disadvantages to thinking of digital as a unit unto-itself is how it can then interact correctly with the non-digital functions that share the same purpose. If you have digital customer servicing and non-digital customer servicing, does it really make sense to have one in a digital department and the other as a customer-service department?

There is a case, however, for incubating digital capabilities within a small compact, standalone entity that can protect and nourish the digital investment with a distinct culture and resourcing model. I get that. Ultimately, though, it seems to me that unless digital OWNS an entire function, separating that function across digital and non-digital lines is arbitrary and likely to be ineffective in an omni-channel world.

But here’s the flip side. If you have a single digital property and it shares marketing and customer support functions, how do you allocate real-estate and who gets to determine key things like site structure? I’ve seen organizations where everything but the homepage is owned by somebody and the home page is like Oliver Twist. “Home page for sale, does anybody want one?”

That’s not optimal.

So the more overlap there needs to be between the functions and your digital properties, the more incentive you have to build a purely digital organization.

No matter what structure you pick, there are some trade-offs you’re going to have to live with. That’s part of why there is no magic answer to the right organization.

But far more important than the precise balance you strike around centralization or even where you put digital is the way you organize the core capabilities that belong to digital. Here, the vast majority of enterprises organize along the same general lines. Digital comprises some rough set of capabilities including:

IT

Creative

Marketing

Customer

UX

Analytics

Testing

VoC

In almost every company I work with, each of these capabilities is instantiated as a separate team. In most organizations, the IT folks are in a completely different reporting structure all the way up. There is no unification till you hit the C-Suite. Often, Marketing and Creative are unified. In some organizations, all of the research functions are unified (VoC, analytics) – sometimes under Customer, sometimes not. UX and Testing can wind up almost anywhere. They typically live under the Marketing department, but they can also live under a Research or Customer function.

None of this, to me, makes any sense.

To do digital well requires a deep integration of these capabilities. What’s more, it requires that these teams work together on a consistent basis. That’s not the way it’s mostly done.

Almost every enterprise I see not only siloes these capabilities, but puts in place budgetary processes that fund each digital asset as a one-time investment and which requires pass-offs between teams.

You want to launch a new website. You hire an agency to design the Website. Then your internal IT team builds it. Now the agency goes away. The folks who designed the website no longer have anything to do with it. What’s more, the folks who built it get rotated onto the next project. Sometimes, that’s all that happens. The website just sits there – unimproved. Sometimes the measurement team will now pick it up. Keep in mind that the measurement team almost never had anything to do with the design of the site in the first place. They are just there to report on it. Still, they measure it and if they find some problem, who do they give it to?

Well, maybe they pass it on to the UX team or the testing team. Those teams, neither of which have ever worked with the website or had anything to do with its design are now responsible for implementing changes on it. And, of course, they will be working with developers who had nothing to do with building it.

Meanwhile, on an entirely separate track, the customer team may be designing a broader experience that involves that website. They enlist the VoC team to survey the site’s users and find out what they don’t like about it. Neither team (of course) had anything to do with designing or building the site.

If they come to some conclusion about what they want the site to do, they work with another(!) team of developers to implement their changes. That these changes may be at cross-purposes to the UX team’s changes or the original design intent is neither here nor there.

Does any of this make sense?

If you take continuous improvement to heart (and you should because it is the key to digital excellence), you need to realize that almost everything about the way your digital organization functions is wrong. You budget wrong and you organize wrong.

Here’s my simple rule about building digital assets. If it’s worth doing, it’s worth improving. Nothing you build will ever be right the first time. Accept that. Embrace it. That means you budget digital teams to build AND improve something. Those teams don’t go away. They don’t rotate. And they include ALL of the capabilities you need to successfully deliver digital experiences. Your developers don’t rotate off, your designers don’t go away, your VoC folks aren’t living in a parallel universe.

When you do things this way, you embody a commitment to continuous improvement deeply into your core organizational processes. It almost forces you to do it right. All those folks in IT and creative will demand analytics and tests to run or they won’t have anything to do.

That’s a good thing.

This type of vertical integration of digital capabilities is far, far more important than the balance around centralization or even the home for digital. Yet it gets far less attention in most enterprise strategic discussions.

The existence or lack of this vertical integration is the single most important factor in driving analytics into digital. Do it right, and you’ll do it well. Do what everyone else does and…well…it won’t be so good.

Building an analytics culture in the enterprise is incredibly important. It’s far more important than any single capability, technology or technique. But building culture isn’t easy. You can’t buy it. You can’t proclaim it. You can’t implement it.

There is, of course, a vast literature on building culture in the enterprise. But if the clumsy, heavy-handed, thoroughly useless attempts to “build culture” that I’ve witnessed over the course of my working life are any evidence, that body of literature is nearly useless.

Here’s one thing I know for sure: you don’t build culture by talk. I don’t care whether it’s getting teenagers to practice safe-sex or getting managers to use analytics, preaching virtue doesn’t work, has never worked and will never work. Telling people to be data-driven, proclaiming your commitment to analytics, touting your analytics capabilities: none of this builds analytics culture.

If there’s one thing that every young employee has learned in this era, it’s that fancy talk is cheap and meaningless. People are incredibly sophisticated about language these days. We can sit in front of the TV and recognize in a second whether we’re seeing a commercial or a program. Most of us can tell the difference between a TV show and movie almost at a glance. We can tune out advertising on a Website as effortlessly as we put on our pants. A bunch of glib words aren’t going to fool anyone. You want to know what the reaction is to your carefully crafted, strategic consultancy driven mission statement or that five year “vision” you spent millions on and just rolled out with a cool video at your Sales Conference? Complete indifference.

That’s if you’re lucky…if you didn’t do it really well, you got the eye-roll.

But it isn’t just that people are incredibly sensitive – probably too sensitive – to BS. It’s that even true, sincere, beautifully reasoned words will not build culture. Reading moral philosophy does not create moral students. Not because the words aren’t right or true, but because behaviors are, for the most part, not driven by those types of reasons.

That’s the whole thing about culture.

Culture is lived, not read or spoken. To create it, you have to ingrain it in people’s thinking. If you want a data-driven organization, you have to create good analytic habits. You have to make the organization (and you too) work right.

How do you do that?

You do it by creating certain kinds of process and behaviors that embed analytic thinking. Do enough of that, and you’ll have an analytic culture. I guarantee it. The whole thrust of this recent series of posts is that by changing the way you integrate analytics, voice-of-customer, journey-mapping and experimentation into the enterprise, you can drive better digital decision making. That’s building culture. It’s my big answer to the question of how you build analytics culture.

But I have some small answers as well. Here, in no particular order, are practical ways you can create importantly good analytics habits in the enterprise.

Analytic Reporting

What it is: Changing your enterprise reporting strategy by moving from reports to tools. Analytic models and forecasting allow you to build tools that integrate historical reporting with forecasting and what-if capabilities. Static reporting is replaced by a set of interactive tools that allow users to see how different business strategies actually play-out.

Why it build analytics culture: With analytics reporting, you democratize knowledge not data. It makes all the difference in the world. The analytic models capture your best insight into how a key business works and what levers drive performance. Building this into tools not only operationalizes the knowledge, it creates positive feedback loops to analytics. When the forecast isn’t right, everyone know it and the business is incented to improve its understanding and predictive capabilities. This makes for better culture in analytics consumers and analytics producers.

Cadence of Communications

What it is: Setting up regular briefings between analytics and your senior team and decision-makers. This can include review of dashboards but should primarily focus on answers to previous business questions and discussion of new problems.

Why it builds analytics culture: This is actually one of the most important things you can do. It exposes decision-makers to analytics. It makes it easy for decision-makers to ask for new research and exposes them to the relevant techniques. Perhaps even more important, it lets decision-makers drive the analytics agenda, exposes analysts to real business problems, and forces analysts to develop better communication skills.

C-Suite Advisor

What it is: Create an Analytics Minister-without-portfolio whose sole job is to advise senior decision-makers on how to use, understand and evaluate the analytics, the data and the decisions they get.

Why it builds analytics culture: Most senior executives are fairly ignorant of the pitfalls in data interpretation and the ins-and-outs of KPIs and experimentation. You can’t send them back to get a modern MBA, but you can give them a trusted advisor with no axe to grind. This not only raises their analytics intelligence, it forces everyone feeding them information to up their game as well. This tactic is also critical because of the next strategy…

Walking the Walk

What it is: Senior Leaders can talk tell they are blue in the face about data-driven decision-making. Nobody will care. But let a Senior Leader even once use data or demand data around a decision they are making and the whole organization will take notice.

Why it builds analytics culture: Senior leaders CAN and DO have a profound impact on culture but they do so by their behavior not their words. When the leaders at the top use and demand data for decisions, so will everyone else.

Tagging Standards

What it is: A clearly defined set of data collection specifications that ensure that every piece of content on every platform is appropriately tagged to collect a rich set of customer, content, and behavioral data.

Why it builds analytics culture: This ends the debate over whether tags and measurement are optional. They aren’t. This also, interestingly, makes measurement easier. Sometimes, people just need to be told what to do. This is like choosing which side of the road to drive on – it’s far more important that you have a standard that which side of the road you pick. Standards are necessary when an organization needs direction and coordination. Tagging is a perfect example.

CMS and Campaign Meta-Data

What it is: The definition of and governance around the creation of campaign and content meta-data. Every piece of content and every campaign element should have detailed, rich meta-data around the audience, tone, approach, contents, and every other element that can be tuned and analyzed.

Why it builds analytics culture: Not only is meta-data the key to digital analytics – providing the meaning that makes content consumption understandable, but rich meta-data definition guides useful thought. These are the categories people will think about when they analyze content and campaign performance. That’s as it should be and by providing these pre-built, populated categorizations, you’ll greatly facilitate good analytics thinking.

Rapid VoC

What it is: The technical and organizational capability to rapidly create, deploy and analyze surveys and other voice-of-customer research instruments.

Why it builds analytics culture: This is the best capability I know for training senior decision-makers to use research. It’s so cheap, so easy, so flexible and so understandable that decision-makers will quickly get spoiled. They’ll use it over and over and over. Well – that’s the point. Nothing builds analytics muscle like use and getting this type of capability deeply embedded in the way your senior team thinks and works will truly change the decision-making culture of the enterprise.

SPEED and Formal Continuous Improvement Cycles

What it is: The use of a formal methodology for digital improvement. SPEED provides a way to identify the best opportunities for digital improvement, the ways to tackle those opportunities, and the ability to measure the impact of any changes. It’s the equivalent of Six Sigma for digital.

Why it builds analytics culture: Formal methods make it vastly easier for everyone in the organization to understand how to get better. Methods also help define a set of processes that organizations can build their organization around. This makes it easier to grow and scale. For large enterprises, in particular, it’s no surprise that formal methodologies like Six Sigma have been so successful. They make key cultural precepts manifest and attach processes to them so that the organizational inertia is guided in positive directions.

Does this seem like an absurdly long list? In truth I’m only about half-way through. But this post is getting LONG. So I’m going to save the rest of my list for next week. Till then, here’s some final thoughts on creating an analytics culture.

The secret to building culture is this: everything you do builds culture. Some things build the wrong kind of culture. Some things the right kind. But you are never not building culture. So if you want to build the right culture to be good at digital and decision-making, there’s no magic elixir, no secret sauce. There is only the discipline of doing things right. Over and over.

That being said, not every action is equal. Some foods are empty of nutrition but empty, too, of harm. Others positively destroy your teeth or your waistline. Still others provide the right kind of fuel. The things I’ve described above are not just a random list of things done right, they are the small to medium things that, done right, have the biggest impacts I’ve seen on building a great digital and analytics culture. They are also targeted to places and decisions which, done poorly, will deeply damage your culture.

I’ll detail some more super-foods for analytics culture in my next post!

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.